R Notebook
Single cell seq after sorting for PhenoID
sample1 = Neurons1 sample2 = Neurons2 sample3 = Glia1 - Astrocytes (CD44+) sample4 = Glia2 - Radial Glia (CD44-)
In HPC I have run steps of scrnabox (custom pipeline in progress) 1. Cell Ranger for feature seq 2. Create Seurat Objects 3. Apply minimum filtering and calculate percent mitochondria.
I have technical 3 replicates with hashtag labels at this point I haven’t yet demultiplex the hashtags. The data here will be treated as one sample. I sorted three separate samples and pooled them together.
# set up the environmentWarning message:
R graphics engine version 15 is not supported by this version of RStudio. The Plots tab will be disabled until a newer version of RStudio is installed. library(Seurat)Registered S3 method overwritten by 'data.table':
method from
print.data.table
Attaching SeuratObject
Attaching splibrary(dplyr)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, unionlibrary(Matrix)
library(ggplot2)
rm(list = ls())
Read in the seurat objects made in compute canada
Glia2 <- readRDS(paste(pathway,"seu4.rds",sep = ""))
Neurons1
An object of class Seurat
33541 features across 3952 samples within 2 assays
Active assay: RNA (33538 features, 0 variable features)
1 other assay present: HTONeurons2An object of class Seurat
33541 features across 34830 samples within 2 assays
Active assay: RNA (33538 features, 0 variable features)
1 other assay present: HTOGlia1An object of class Seurat
33541 features across 54723 samples within 2 assays
Active assay: RNA (33538 features, 0 variable features)
1 other assay present: HTOGlia2An object of class Seurat
33541 features across 10338 samples within 2 assays
Active assay: RNA (33538 features, 0 variable features)
1 other assay present: HTOHave a look at the objects that already have some filtering
See the violin plots
VlnPlot(Neurons1, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
VlnPlot(Neurons1, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 500)Warning: Removed 873 rows containing non-finite values (stat_ydensity).
Warning: Removed 873 rows containing missing values (geom_point).
VlnPlot(Neurons1, pt.size = 0.10, features = c("nCount_RNA"), y.max = 2000)Warning: Removed 546 rows containing non-finite values (stat_ydensity).
Warning: Removed 546 rows containing missing values (geom_point).
# filter more cells
Neuron1.ft <- subset(Neurons1, subset = nFeature_RNA > 250 & nCount_RNA > 250 & nCount_RNA < 10000)
Neuron1.ftAn object of class Seurat
33541 features across 1833 samples within 2 assays
Active assay: RNA (33538 features, 0 variable features)
1 other assay present: HTONeurons 2 - CD56++
VlnPlot(Neurons2, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
VlnPlot(Neurons2, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 500)Warning: Removed 5653 rows containing non-finite values (stat_ydensity).
Warning: Removed 5653 rows containing missing values (geom_point).
VlnPlot(Neurons2, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 1000)Warning: Removed 2379 rows containing non-finite values (stat_ydensity).
Warning: Removed 2379 rows containing missing values (geom_point).
VlnPlot(Neurons2, pt.size = 0.10, features = c("nCount_RNA"), y.max = 2000)Warning: Removed 2264 rows containing non-finite values (stat_ydensity).
Warning: Removed 2264 rows containing missing values (geom_point).
# filter more cells
Neuron2.ft <- subset(Neurons2, subset = nFeature_RNA > 500 & nCount_RNA > 500 & nCount_RNA < 10000)
Neuron2.ftAn object of class Seurat
33541 features across 5190 samples within 2 assays
Active assay: RNA (33538 features, 0 variable features)
1 other assay present: HTOGlia1 - Astrocyte data
Glia1
An object of class Seurat
33541 features across 54723 samples within 2 assays
Active assay: RNA (33538 features, 0 variable features)
1 other assay present: HTOGlia2 - Radial Glia
## Filter Glia 2
VlnPlot(Glia2, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
VlnPlot(Glia2, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 5000)Warning: Removed 61 rows containing non-finite values (stat_ydensity).
Warning: Removed 61 rows containing missing values (geom_point).
VlnPlot(Glia2, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 1000)Warning: Removed 2435 rows containing non-finite values (stat_ydensity).
Warning: Removed 2435 rows containing missing values (geom_point).
VlnPlot(Glia2, pt.size = 0.10, features = c("nCount_RNA"), y.max = 1000)Warning: Removed 3194 rows containing non-finite values (stat_ydensity).
Warning: Removed 3194 rows containing missing values (geom_point).
VlnPlot(Glia2, pt.size = 0.10, features = c("nCount_RNA"), y.max = 12000)Warning: Removed 199 rows containing non-finite values (stat_ydensity).
Warning: Removed 199 rows containing missing values (geom_point).
# extreme filter
Glia2.ft <- subset(Glia1, subset = nFeature_RNA > 500 & nCount_RNA > 500 & nCount_RNA < 10000)
Glia2.ftAn object of class Seurat
33541 features across 37813 samples within 2 assays
Active assay: RNA (33538 features, 0 variable features)
1 other assay present: HTOVlnPlot(Glia1.ft, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
NA
NA
NA
NAAnalyze each dataset - get clusters
plot3 <- ElbowPlot(seu,ndims = 50)
plot3
plot2
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Removed 13701 rows containing missing values (geom_point).
plot
# umap
seu <- RunUMAP(seu, reduction = "pca", n.neighbors = 43, dims = 1:25)Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
21:40:56 UMAP embedding parameters a = 0.9922 b = 1.112
21:40:56 Read 1833 rows and found 25 numeric columns
21:40:56 Using Annoy for neighbor search, n_neighbors = 43
21:40:56 Building Annoy index with metric = cosine, n_trees = 50
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:40:56 Writing NN index file to temp file /var/folders/k4/khtkczkd5tn732ftjpwgtr240000gn/T//RtmpgWVi60/file1138f299a329a
21:40:56 Searching Annoy index using 1 thread, search_k = 4300
21:40:56 Annoy recall = 100%
21:40:56 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 43
21:40:57 Initializing from normalized Laplacian + noise (using irlba)
21:40:57 Commencing optimization for 500 epochs, with 107418 positive edges
Using method 'umap'
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:41:00 Optimization finishedDimPlot(seu, reduction = "umap", group.by = "orig.ident")
NA
NA
NAMake the clusters Neurons1
Find the cluster markers for Neurons1
Idents(seu) <- 'RNA_snn_res.0.2'
ClusterMarkers <- FindAllMarkers(seu)Calculating cluster 0
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=09s top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(seu, features = top5$gene, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.2')
#write.csv(ClusterMarkers, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/ClusterMarkers_neurons1_res025.csv")
write.csv(ClusterMarkers, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/ClusterMarkers_neurons1_res02.csv")
DimPlot(seu, group.by = 'RNA_snn_res.0.2', reduction = 'umap')
Annotate clusters Use: Organoid data, public brain data (LaManno, Lake, Mascako)
Merge the files
Idents(Neurons1ft) <- 'orig.ident'
Neurons1ft$orig.ident <- 'Neurons1'
Idents(Neurons2ft) <- 'orig.ident'
Neurons2ft$orig.ident <- 'Neurons2'
Idents(Glia1ft) <- 'orig.ident'
Glia1ft$orig.ident <- 'Glia1'
Idents(Glia2ft) <- 'orig.ident'
Glia2ft$orig.ident <- 'Glia2'
sorted.merge <- merge(Neurons1ft, y=c(Neurons2ft,Glia1ft,Glia2ft), add.cell.ids = c("Neurons1","Neurons2","Glia1","Glia2"), project = "SortedCells")
sorted.mergeAn object of class Seurat
33544 features across 138206 samples within 2 assays
Active assay: RNA (33538 features, 0 variable features)
1 other assay present: HTOtable(all.ft$orig.ident)
Glia1 Glia2 Neurons1 Neurons2
55268 10429 3418 29575 Prepare for the clustering
all.ft <- ScaleData(all.ft)Centering and scaling data matrix
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|=============================================================================================| 100%all.ft <- RunPCA(all.ft)PC_ 1
Positive: STMN2, CD24, CHGB, CELF4, GRIA2, CACNA2D1, EOMES, INSM1, PCAT4, NEUROD1
MARCH1, GAP43, RASGRP1, MIAT, STMN4, PCSK2, IMPG2, CHGA, INA, MGAT4C
CDHR1, NRN1, GNG8, ST18, IGFBPL1, DDC, LHX1, SLC17A6, NEUROG1, PKIB
Negative: VIM, COL3A1, LGALS1, IGFBP7, DCN, APOE, COL1A1, SPARC, IGFBP4, MDK
TIMP1, GSTP1, HSP90B1, S100A11, COL1A2, IGFBP5, TMSB10, GAPDH, S100A6, SPATS2L
RPL7A, CALD1, ZFP36L1, IGFBP2, RPL10, FTL, SPARCL1, FABP5, S100A10, IFITM3
PC_ 2
Positive: COL3A1, DCN, COL1A1, IGFBP7, COL1A2, APOE, VIM, HPD, IGFBP4, CP
FABP5, FTL, S100A11, S100A6, VCAN, NDUFA4L2, LGALS1, WFIKKN2, COL6A3, MGP
TIMP1, IFITM3, LUM, CYP1B1, RPS12, CRYAB, ID3, CXCL14, WIF1, APOC1
Negative: CELF4, CACNA2D1, INA, STMN2, CHGB, PCDH9, ANK3, MARCH1, EOMES, GRIA2
SOX4, DCX, SSTR2, STMN1, NEUROD1, TENM3, INSM1, ATCAY, OLFM1, STMN4
CRMP1, SYT1, GAP43, ATP1A3, NCKAP5, AMER2, PCLO, ELAVL3, CA10, NRXN1
PC_ 3
Positive: COL3A1, COL1A2, COL1A1, VCAN, COL6A3, S100A4, LUM, DCN, COL4A1, COL4A2
OGN, EDNRA, COL6A1, COL5A2, APOD, PRRX1, COL6A2, IGFBP4, FN1, MFAP4
NDUFA4L2, THY1, TNFAIP6, PCOLCE, COL15A1, COL5A1, IFI16, PLAC9, CD248, BGN
Negative: PTPRZ1, WLS, SOX2, GABBR2, CNTNAP2, SPARCL1, LY6H, CLU, GDF10, CRYAB
SERPINI1, S100B, WNT2B, ESM1, SLC2A1, ITM2C, LIX1, TPBG, RFX4, CBLN1
RSPO2, MEGF10, NELL2, MGST1, SCG2, HPD, VAT1L, ABCA8, PLTP, SLC1A3
PC_ 4
Positive: WFIKKN2, CP, ECEL1, SERPINF1, KRT18, ID1, PCP4, CYP1B1, RBP1, IGFBP7
TPBG, KRT8, FHIT, WIF1, CA2, TMSB4X, BMP4, HPD, FOLR1, CRABP2
SPINT2, EFEMP1, CXCL14, KCNJ13, ASCL1, GPNMB, MDK, EMX2, SLC4A10, RPS12
Negative: PTN, PTPRZ1, GABBR2, MEGF10, DLK1, SCG2, ATP1A2, FN1, SGCD, SOX2
ABCA8, NTRK2, RFX4, ESM1, COL4A1, MARCKS, COL4A2, VCAN, VAT1L, GRIN2A
NRIP3, SCRG1, HAP1, OGN, SORCS2, EDNRB, SFRP2, PLP1, LUM, FFAR4
PC_ 5
Positive: TPBG, FN1, OGN, DKK3, IFI16, MSX1, CXCL14, OLFML2A, CA2, PAPPA2
NID1, SYNE2, ENPP2, SLC4A10, CDC42EP3, KCNJ13, IGF1, GPHN, IGFBP2, ZEB2
EXPH5, NOV, TRPM3, HTR2C, GPNMB, MPZ, CYP1B1, CADM2, SLC5A3, PIK3R1
Negative: APOE, APOC1, DCN, FABP5, DLK1, PTN, KDR, PRSS56, CLU, IGFBP4
BST2, TFPI2, S100A6, PTGDS, CITED4, COL23A1, RPL10, FTL, SLC7A2, IFITM3
FABP4, NDUFA4L2, GNG11, RARRES1, FXYD5, SCG2, WFDC1, TMEM176B, RPL7A, TMEM176A plot <- VizDimLoadings(all.ft, dims = 1:2, reduction = "pca")#SaveFigure(plot, "viz_PCA_loadings", width = 10, height = 8)
plot
NA
NAplot <- DimPlot(all.ft, reduction = "pca", group.by = "orig.ident")
plot
plot <- ElbowPlot(all.ft,ndims = 50)
plot
# best is the
VlnPlot(all.ft, features = c("NCAM1","CD44","CD24","AQP4","TH"), ncol = 3, group.by = 'orig.ident')
The expression of the selected markers are not very revealing - there are neuronal markers in the Glia
I wish I had done an unsorted sample
Now I have all 4 samples I want to try the integration steps
Warning message:
R graphics engine version 15 is not supported by this version of RStudio. The Plots tab will be disabled until a newer version of RStudio is installed. all.ft <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/cellrangerOuts/AllcellsObjectSept20filtered.Rds")Registered S3 method overwritten by 'data.table':
method from
print.data.table
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudioall.int <- IntegrateData(anchorset = all.anchors)
Merging dataset 1 into 2
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 4 into 3
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 2 1 into 3 4
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating dataNow that I have the integrated object I will continue to follow the standard scRNAseq to workflow and identify cell types.
Cluster
all.int <- FindNeighbors(all.int, dims = 1:30, k.param = 275)Computing nearest neighbor graph
Computing SNNTry on the not integrated object - just merge
all.ft <- RunUMAP(all.ft, reduction = "pca", n.neighbors = 30, dims = 1:21)
16:34:29 UMAP embedding parameters a = 0.9922 b = 1.112
16:34:29 Read 92644 rows and found 21 numeric columns
16:34:29 Using Annoy for neighbor search, n_neighbors = 30
16:34:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:34:35 Writing NN index file to temp file /var/folders/k4/khtkczkd5tn732ftjpwgtr240000gn/T//RtmpxdKhUe/file114bc936cbd
16:34:35 Searching Annoy index using 1 thread, search_k = 3000
16:35:03 Annoy recall = 100%
16:35:04 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
16:35:08 Initializing from normalized Laplacian + noise (using irlba)
16:35:16 Commencing optimization for 200 epochs, with 4273678 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:36:16 Optimization finishedDimPlot(all.ft, reduction = "umap", group.by = "orig.ident")
Find the clusters
all.ft <- FindClusters(all.ft, resolution = 0.3)
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 92644
Number of edges: 2838478
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8755
Number of communities: 9
Elapsed time: 34 seconds#clustree(all.int, prefix = "RNA_snn_res.")
DimPlot(all.ft, reduction = "umap")
Transfer labels from other Midbrain organoid data (165 days)
# this is the reference data
MBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AST23_BrainComm/MBOclusters_names29072021.rds")
Idents(MBO) <- "cluster_labels"
DefaultAssay(all.ft) <- "RNA"
DefaultAssay(MBO) <- "RNA"
MO.query <- all.ft
# find the reference anchors
print("finding reference anchors")[1] "finding reference anchors"MO.anchors <- FindTransferAnchors(reference = MBO ,query = MO.query, dims = 1:30)Performing PCA on the provided reference using 2000 features as input.
Projecting cell embeddings
Finding neighborhoods
Finding anchors
Found 1965 anchors
Filtering anchors
Retained 588 anchors# can try a range of dims 20-50
print("getting predictions")[1] "getting predictions"predictions <- TransferData(anchorset = MO.anchors, refdata = MBO$cluster_labels)Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labelsMO.query <- AddMetaData(MO.query, metadata = predictions)
print(table(MO.query$predicted.id))
Epithelial Neural Precursors Neurons-DA Neurons-e Oligodendrocytes
1152 396 943 13239 59074
RGa RGd1 RGd2
14 17776 50 See the predictions
DimPlot(MO.query, reduction = "umap", group.by = 'predicted.id')
NA
NASome heatmaps
See the usual marker lists - by sorted population, by clusters
Markers.glia1Vs2 <- FindMarkers(all.ft, ident.1 = c('Glia1'), ident.2 = c('Glia2'))
| | 0 % ~calculating
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 09s# Compare Neurons1 vs Neurons2
Markers.neurons1vs2 <- FindMarkers(all.ft, ident.1 = c('Neurons1'), ident.2 = c('Neurons2'))For a more efficient implementation of the Wilcoxon Rank Sum Test,
(default method for FindMarkers) please install the limma package
--------------------------------------------
install.packages('BiocManager')
BiocManager::install('limma')
--------------------------------------------
After installation of limma, Seurat will automatically use the more
efficient implementation (no further action necessary).
This message will be shown once per session
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=18s Check cell types using EnrichR
---
title: "R Notebook"
output: html_notebook
---
Single cell seq after sorting for PhenoID

sample1 = Neurons1
sample2 = Neurons2
sample3 = Glia1 - Astrocytes (CD44+)
sample4 = Glia2 - Radial Glia (CD44-)

In HPC I have run steps of scrnabox (custom pipeline in progress)
1. Cell Ranger for feature seq
2. Create Seurat Objects 
3. Apply minimum filtering and calculate percent mitochondria.

I have technical 3 replicates with hashtag labels at this point I haven't yet demultiplex the hashtags. The data here will be treated as one sample.  I sorted three separate samples and pooled them together. 


```{r}
# set up the environment

library(Seurat)
library(dplyr)
library(Matrix)
library(ggplot2)

rm(list = ls())


```


Read in the seurat objects made in compute canada

```{r}

# this seems to never load I'll use step 3 output that has some filtering 
# nFeature_RNA > 180 and percent.mt < 25

pathway <- "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/"

Neurons1 <- readRDS(paste(pathway,"seu1.rds",sep = ""))
Neurons2 <- readRDS(paste(pathway,"seu2.rds",sep = ""))
Glia1 <- readRDS(paste(pathway,"seu3.rds",sep = ""))
Glia2 <- readRDS(paste(pathway,"seu4.rds",sep = ""))

Neurons1
Neurons2
Glia1
Glia2

```


Have a look at the objects that already have some filtering


See the violin plots 

```{r}

VlnPlot(Neurons1, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

VlnPlot(Neurons1, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 500)
VlnPlot(Neurons1, pt.size = 0.10, features = c("nCount_RNA"), y.max = 2000)



# filter more cells

Neuron1.ft <- subset(Neurons1, subset = nFeature_RNA > 250 & nCount_RNA > 250 & nCount_RNA < 10000) 
Neuron1.ft

# 33541 features across 1833 samples


```

Neurons 2 - CD56++

```{r}

VlnPlot(Neurons2, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
VlnPlot(Neurons2, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 500)
VlnPlot(Neurons2, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 1000)
VlnPlot(Neurons2, pt.size = 0.10, features = c("nCount_RNA"), y.max = 2000)



# filter more cells

Neuron2.ft <- subset(Neurons2, subset = nFeature_RNA > 500 & nCount_RNA > 500 & nCount_RNA < 10000) 
Neuron2.ft



```

Glia1 - Astrocyte data

```{r}

VlnPlot(Glia1, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

VlnPlot(Glia1, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 5000)
VlnPlot(Glia1, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 1000)
VlnPlot(Glia1, pt.size = 0.10, features = c("nCount_RNA"), y.max = 1000)
VlnPlot(Glia1, pt.size = 0.10, features = c("nCount_RNA"), y.max = 12000)


# extreme filter

Glia1.ft <- subset(Glia1, subset = nFeature_RNA > 500 & nCount_RNA > 300 & nCount_RNA < 10000) 
Glia1.ft
Glia1

VlnPlot(Glia1.ft, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)





```


Glia2 - Radial Glia

```{r}

## Filter Glia 2
VlnPlot(Glia2, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

VlnPlot(Glia2, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 5000)
VlnPlot(Glia2, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 1000)
VlnPlot(Glia2, pt.size = 0.10, features = c("nCount_RNA"), y.max = 1000)
VlnPlot(Glia2, pt.size = 0.10, features = c("nCount_RNA"), y.max = 12000)


# extreme filter

Glia2.ft <- subset(Glia1, subset = nFeature_RNA > 500 & nCount_RNA > 500 & nCount_RNA < 10000) 
Glia2.ft


VlnPlot(Glia1.ft, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

# there are so many suposed cells I am concerned the high read cells are actually doublets. 



```


Analyze each dataset - get clusters 

```{r}

# cluster the neurons
seu <- Neuron1.ft
seu$orig.ident <- 'Neurons1'

seu <- NormalizeData(seu, normalization.method = "LogNormalize", scale.factor = 10000)
seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2000)
# Identify the 10 most highly variable genes
top10 <- head(VariableFeatures(seu), 10)
# plot variable features with and without labels
plot1 <- VariableFeaturePlot(seu)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)



seu <- ScaleData(seu)
seu <- RunPCA(seu)
Idents(seu) <- 'orig.ident'
plot <- DimPlot(seu, reduction = "pca")


plot3 <- ElbowPlot(seu,ndims = 50)
plot3

plot2
plot



```

```{r}

# umap

seu <- RunUMAP(seu, reduction = "pca", n.neighbors = 43, dims = 1:25)
DimPlot(seu, reduction = "umap", group.by = "orig.ident")



```

Make the clusters Neurons1

```{r}

seu <- FindNeighbors(seu, dims = 1:25, k.param = 43)
seu <- FindClusters(seu, resolution = c(0,0.2,0.25,0.5,0.8))
seu <- FindClusters(seu, resolution = c(1.2))

library(clustree)
clustree(seu, prefix = "RNA_snn_res.")
DimPlot(seu)

```

```{r}
# look a lot at the clusers

VlnPlot(seu, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), group.by = 'seurat_clusters', ncol = 1)
# these cells might be grouping by - how much MT and number of Features
# clusters 4,5,6 have more features

VlnPlot(seu, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), group.by = 'RNA_snn_res.0.25', ncol = 1)
# here cluster 3 has higher expression, cluster 1 and 4 have similar Features RNA
# cluster 0 has higher percent MT levels

VlnPlot(seu, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), group.by = 'RNA_snn_res.0.5', ncol = 1)
VlnPlot(seu, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), group.by = 'RNA_snn_res.0.2', ncol = 1)
# now only cluster 0 have high MT and low features, clusters 1,2,3 have simiular RNA and Counts

```

Find the cluster markers for Neurons1

```{r}
Idents(seu) <- 'RNA_snn_res.0.2'
ClusterMarkers <- FindAllMarkers(seu)

top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(seu, features = top5$gene, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.2')


#write.csv(ClusterMarkers, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/ClusterMarkers_neurons1_res025.csv")


write.csv(ClusterMarkers, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/ClusterMarkers_neurons1_res02.csv")

```

```{r}
DimPlot(seu, group.by = 'RNA_snn_res.0.2', reduction = 'umap')

```






Annotate clusters
Use: Organoid data, public brain data (LaManno, Lake, Mascako)


```{r}

# this is some reference data

# SNCA and control midbrain organoids 165 days in culture
MBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AST23_BrainComm/MBOclusters_names29072021.rds")

# Midbrain  AIW002 120 days in culture
AIWMBO <- "/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio120days/MOintegratedClusterK123res0.8.names_nov16_2021"
# DA neuron subtypes from postmortem brain Kamath et al 2022
DAsubtypes <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/Macosko_Data/PD_da.Rds")

Idents(MBO) <- "cluster_labels"
DefaultAssay(MBO) <- "RNA"

#first predict with the MBO data

MO.query <-seu
# find the reference anchors
print("finding reference anchors")
MO.anchors <- FindTransferAnchors(reference = MBO ,query = MO.query, dims = 1:25)
# can try a range of dims 20-50
print("getting predictions")
predictions <- TransferData(anchorset = MO.anchors, refdata = MBO$cluster_labels)
MO.query <- AddMetaData(MO.query, metadata = predictions)
print(table(MO.query$predicted.id))

seu.q <- MO.query

seu.q <- FindClusters(seu.q, resolution = c(1.2))

## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.1.2, seu.q$predicted.id))
pr.t.lables <- as.data.frame(prop.table(table(seu.q$RNA_snn_res.1.2, seu.q$predicted.id)))
t.lables$Freq <- as.double(t.lables$Freq)


# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")



```













Merge the files

```{r}
Idents(Neurons1ft) <- 'orig.ident'
Neurons1ft$orig.ident <- 'Neurons1'
Idents(Neurons2ft) <- 'orig.ident'
Neurons2ft$orig.ident <- 'Neurons2'
Idents(Glia1ft) <- 'orig.ident'
Glia1ft$orig.ident <- 'Glia1'
Idents(Glia2ft) <- 'orig.ident'
Glia2ft$orig.ident <- 'Glia2'


sorted.merge <- merge(Neurons1ft, y=c(Neurons2ft,Glia1ft,Glia2ft), add.cell.ids = c("Neurons1","Neurons2","Glia1","Glia2"), project = "SortedCells")
sorted.merge

saveRDS(sorted.merge, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/cellrangerOuts/mergedObjectSept19.Rds")

sorted.merge <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/cellrangerOuts/mergedObjectSept19.Rds")


```




```{r}

all.ft <- NormalizeData(all.ft, normalization.method = "LogNormalize", scale.factor = 10000)
all.ft <- FindVariableFeatures(all.ft, selection.method = "vst", nfeatures = 2000)
# Identify the 10 most highly variable genes
top10 <- head(VariableFeatures(all.ft), 10)
# plot variable features with and without labels
plot1 <- VariableFeaturePlot(all.ft)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
plot2

```


Prepare for the clustering

```{r}

all.ft <- ScaleData(all.ft)
all.ft <- RunPCA(all.ft)
plot <- VizDimLoadings(all.ft, dims = 1:2, reduction = "pca")
#SaveFigure(plot, "viz_PCA_loadings", width = 10, height = 8)
plot


```

```{r}
plot <- DimPlot(all.ft, reduction = "pca", group.by = "orig.ident")

plot
```


```{r}

plot <- ElbowPlot(all.ft,ndims = 50)
plot

# best is the 21

```


```{r}
VlnPlot(all.ft, features = c("NCAM1","CD44","CD24","AQP4","TH"), ncol = 3, group.by = 'orig.ident')

```

The expression of the selected markers are not very revealing - there are neuronal markers in the Glia


I wish I had done an unsorted sample

Now I have all 4 samples I want to try the integration steps


```{r}
# I keep getting a memory error for integration.  I'll save the processed filtered object now.  

saveRDS(all.ft, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/cellrangerOuts/AllcellsObjectSept20filtered.Rds")

all.ft <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/cellrangerOuts/AllcellsObjectSept20filtered.Rds")



```





```{r}

# integrate the datasets
all.list <- SplitObject(all.ft, split.by = "orig.ident")

# Seurat recommends normalizing and finding variable features before integration
for (i in 1:length(all.list)){
  all.list[[i]] <- NormalizeData(all.list[[i]], verbose = FALSE)
  all.list[[i]] <- FindVariableFeatures(all.list[[i]], selection.method = "vst")
}

# This object was done with the normalization
all.anchors <- FindIntegrationAnchors(object.list = all.list, reduction = "rpca", scale = FALSE)


saveRDS(all.int, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/cellrangerOuts/mergedIntObjectSept19.Rds")


saveRDS(all.anchors,"/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/cellrangerOuts/allanchors.Rds")

all.anchors <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/cellrangerOuts/allanchors.Rds")

all.int <- IntegrateData(anchorset = all.anchors)

```


Now that I have the integrated object I will continue to follow the standard scRNAseq to workflow and identify cell types. 


```{r}

# the first object - no normalization before integration
DefaultAssay(all.int) <= "integrated"
all.int <- ScaleData(all.int,verbose = FALSE)
all.int <- RunPCA(all.int, npcs = 30, verbose = FALSE)

ElbowPlot(all.int,ndims = 30)

all.int <- RunUMAP(all.int, reduction = "pca", n.neighbors = 275, dims = 1:30)
DimPlot(all.int, reduction = "umap", group.by = "orig.ident")


```



Cluster 

```{r}

all.int <- FindNeighbors(all.int, dims = 1:30, k.param = 275)
all.int <- FindClusters(all.int, resolution = c(0,0.05,0.25,0.5,0.8))
clustree(all.int, prefix = "RNA_snn_res.")


```



Try on the not integrated object - just merge

```{r}



all.ft <- ScaleData(all.ft, verbose = FALSE)
all.ft <- RunPCA(all.ft, npcs = 30, verbose = FALSE)

ElbowPlot(all.ft,ndims = 30)

all.ft <- RunUMAP(all.ft, reduction = "pca", n.neighbors = 30, dims = 1:21)
DimPlot(all.ft, reduction = "umap", group.by = "orig.ident")

```


Find the clusters

```{r}

all.ft <- FindNeighbors(all.ft, dims = 1:21, k.param = 30)
all.ft <- FindClusters(all.ft, resolution = 0.3)
#clustree(all.int, prefix = "RNA_snn_res.")
DimPlot(all.ft, reduction = "umap")


```




Transfer labels from other Midbrain organoid data (165 days)


```{r}
# this is some reference data

# SNCA midbrain organoids
MBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AST23_BrainComm/MBOclusters_names29072021.rds")
#
# DA neuron subtypes from postmortem brain Kamath et al 2022
DAsubtypes <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/Macosko_Data")

Idents(MBO) <- "cluster_labels"
DefaultAssay(MBO) <- "RNA"


MO.query <-
# find the reference anchors
print("finding reference anchors")
MO.anchors <- FindTransferAnchors(reference = MBO ,query = MO.query, dims = 1:30)
# can try a range of dims 20-50
print("getting predictions")
predictions <- TransferData(anchorset = MO.anchors, refdata = MBO$cluster_labels)
MO.query <- AddMetaData(MO.query, metadata = predictions)


print(table(MO.query$predicted.id))




```


See the predictions


```{r}

DimPlot(MO.query, reduction = "umap", group.by = 'predicted.id')


```

Some heatmaps

See the usual marker lists - by sorted population, by clusters

```{r}



Idents(all.ft) <- 'orig.ident'
ClusterMarkers <- FindAllMarkers(all.ft)

top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(all.ft, features = top5$gene, size=3, angle =90, group.bar.height = 0.02, group.by = 'orig.ident')


write.csv(ClusterMarkers, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/cellrangerOuts/ClusterMarkers_4samples_FindAll.csv")

# Find markers Glia vs Neurons

Markers.gliaVneuron <- FindMarkers(all.ft, ident.1 = c('Glia1','Glia2'), ident.2 = c('Neurons1','Neurons2'))
top5.gn <- Markers.gliaVneuron %>% top_n(n=-5, wt = avg_log2FC)
write.csv(ClusterMarkers, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/cellrangerOuts/ClusterMarkers_GliaVsNeurons.csv")

# Compare Glia1 vs Glia2
Markers.glia1Vs2 <- FindMarkers(all.ft, ident.1 = c('Glia1'), ident.2 = c('Glia2'))
write.csv(ClusterMarkers, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/cellrangerOuts/ClusterMarkers_Glia1sGlia2.csv")

# Compare Neurons1 vs Neurons2
Markers.neurons1vs2 <- FindMarkers(all.ft, ident.1 = c('Neurons1'), ident.2 = c('Neurons2'))
write.csv(ClusterMarkers, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/cellrangerOuts/ClusterMarkers_Neurons1VsNeurons2.csv")

```

Check cell types using EnrichR

```{r}

library(devtools)
install_github("wjawaid/enrichR")
library(enrichR)


setEnrichrSite("Enrichr") # Human genes
# list of all the databases

dbs <- listEnrichrDbs()
dbs
# libaries with cell types

db <- c('Allen_Brain_Atlas_10x_scRNA_2021', 'Descartes_Cell_Types_and_Tissue_2021',
        'CellMarker_Augmented_2021','Azimuth_Cell_Types_2021')

# enrichr(genes, databases = NULL)

genes.up.neur <- ClusterMarkers %>% filter(cluster == 'Neurons1' & avg_log2FC > 0)
genes <- genes.up.neur$gene

neurons.En <- enrichr(genes, databases = db)

plotEnrich(neurons.En[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(neurons.En[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(neurons.En[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

neurons.En[[3]]



```


